Probability density estimation of photometric redshifts based on machine learning

Photometric redshifts (photo-z's) provide an alternative way to estimate the distances of large samples of galaxies and are therefore crucial to a large variety of cosmological problems. Among the various methods proposed over the years, supervised machine learning (ML) methods capable to interpolate the knowledge gained by means of spectroscopical data have proven to be very effective. METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts) is a novel method designed to provide a reliable PDF (Probability density Function) of the error distribution of photometric redshifts predicted by ML methods. The method is implemented as a modular workflow, whose internal engine for photo-z estimation makes use of the MLPQNA neural network (Multi Layer Perceptron with Quasi Newton learning rule), with the possibility to easily replace the specific machine learning model chosen to predict photo-z's. After a short description of the software, we present a summary of results on public galaxy data (Sloan Digital Sky Survey - Data Release 9) and a comparison with a completely different method based on Spectral Energy Distribution (SED) template fitting.Comment: 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 784995

METAPHOR: Probability density estimation for machine learning based photometric redshifts

We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z's and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF's derived from a traditional SED template fitting method (Le Phare).Comment: proceedings of the International Astronomical Union, IAU-325 symposium, Cambridge University pres

Anomaly detection in Astrophysics: a comparison between unsupervised Deep and Machine Learning on KiDS data

Every field of Science is undergoing unprecedented changes in the discovery process, and Astronomy has been a main player in this transition since the beginning. The ongoing and future large and complex multi-messenger sky surveys impose a wide exploiting of robust and efficient automated methods to classify the observed structures and to detect and characterize peculiar and unexpected sources. We performed a preliminary experiment on KiDS DR4 data, by applying to the problem of anomaly detection two different unsupervised machine learning algorithms, considered as potentially promising methods to detect peculiar sources, a Disentangled Convolutional Autoencoder and an Unsupervised Random Forest. The former method, working directly on images, is considered potentially able to identify peculiar objects like interacting galaxies and gravitational lenses. The latter instead, working on catalogue data, could identify objects with unusual values of magnitudes and colours, which in turn could indicate the presence of singularities.Comment: Preprint version of the manuscript to appear in the Volume "Intelligent Astrophysics" of the series "Emergence, Complexity and Computation", Book eds. I. Zelinka, D. Baron, M. Brescia, Springer Nature Switzerland, ISSN: 2194-728

Anomaly Detection in Astrophysics: A Comparison Between Unsupervised Deep and Machine Learning on KiDS Data

Every field of Science is undergoing unprecedented changes in the discovery process, and Astronomy has been a main player in this transition since the beginning. The ongoing and future large and complex multi-messenger sky surveys impose a wide exploiting of robust and efficient automated methods to classify the observed structures and to detect and characterize peculiar and unexpected sources. We performed a preliminary experiment on KiDS DR4 data, by applying to the problem of anomaly detection two different unsupervised machine learning algorithms, considered as potentially promising methods to detect peculiar sources, a Disentangled Convolutional Autoencoder and an Unsupervised Random Forest. The former method, working directly on images, is considered potentially able to identify peculiar objects like interacting galaxies and gravitational lenses. The latter instead, working on catalogue data, could identify objects with unusual values of magnitudes and colours, which in turn could indicate the presence of singularities

Effect of feed supplementation with Origanum vulgare L. essential oil on sea bass (Dicentrarchus labrax): A preliminary framework on metabolic status and growth performances

This study provided a preliminary framework for the effects of Origanum vulgare L. essential oil (EO) on sea bass (Dicentrarchus labrax) health status over a 60-day feeding trial. Fish were fed twice a day until apparent satiety with three different diets: a control diet (CD), and two experimental diets supplemented with 100 (D100) and 200 (D200) ppm of oregano EO. No mortality was observed in each treatment. Feeding on D100 diet resulted in high growth performances and better food conversion and protein efficiency ratios. Additionally, the supplementation of 100 ppm EO diet also improved (P < 0.05) hepatosomatic and viscerosomatic indices, compared both to control and D200 diets. EO feeding positively affected (P < 0.05) several serum biochemical indices (amylase activity and total proteins, glucose, triglycerides, and cholesterol levels). Focusing on the antioxidant potential of blood, D100 led to the highest (P < 0.05) ferric reducing antioxidant power values and the lowest (P < 0.05) thiobarbituric acid-reactive substances levels in blood

Pharmacokinetics and mammary elimination of imidocarb in sheep and goats.

The pharmacokinetics and mammary excretion of imidocarb dipropionate, a therapeutic/prophylactic agent against a variety of tick-borne hemoparasitic diseases in domestic animals, have been investigated in sheep and goats. A commercial formulation of imidocarb di-propionate was injected i.m. at a single dose of 3 mg/kg of body weight in 7 mature lactating ewes and 8 lactating does in good health. Blood samples were collected for 48 h after administration and milk samples were collected every 12 h for 10 d. A weak cation-exchange solid-phase procedure was used to remove imidocarb from plasma. A hexane/isoamyl alcohol liquid-liquid procedure was adopted to extract the drug from the milk of sheep. The same method was used for goat milk after exposing the matrices to enzymatic digestion. The extracted samples were analyzed by HPLC. The i.m. disposition kinetics of imidocarb in the 2 species showed significant differences in the rate of elimination (0.0075 +/- 0.002 and 0.025 +/- 0.004 L/h in sheep and goats, respectively), being faster in ewes than in does. Nevertheless, a smaller area under the concentration-time curve (12.21 +/- 0.76 and 9.49 +/- 0.54 microg/mL per h in sheep and goats, respectively), a larger volume of distribution (4.18 +/- 0.44 and 7.68 +/- 0.57 L/kg in sheep and goats, respectively), and a longer mean residence time (9.07 +/- 0.77 and 14.75 +/- 2.20 h in sheep and goats, respectively) were found in goats, suggesting a more rapid and effective drug storage in tissues during the first 48 h after the injection. The concentrations of imidocarb in milk of both species were higher than in plasma. However, a fast passage through the blood-milk barrier and a high storage of imidocarb were observed in the milk of ewes, whereas the drug concentrations were not as high nor was the extent of drug penetration from blood to milk as great in the milk of goats (AUC(milk 0-48)/AUC(plasma 0-48) = 2.5 +/- 0.45 and 1.26 +/- 0.27 in sheep and goat, respectively). Despite the differences in pharmacokinetic behavior, and considering the sensitivity of pathogens to imidocarb, the same dosage regimen can be used for clinical efficacy against Babesia spp. infection in both species. In contrast, the differences in depletion of imidocarb residue in milk and the large variability in mammary drug elimination found in goats suggests that great care should be taken in defining the withdrawal time in small ruminant dairy species

Statistical analysis of probability density functions for photometric redshifts through the KiDS-ESO-DR3 galaxies

Despite the high accuracy of photometric redshifts (zphot) derived using Machine Learning (ML) methods, the quantification of errors through reliable and accurate Probability Density Functions (PDFs) is still an open problem. First, because it is difficult to accurately assess the contribution from different sources of errors, namely internal to the method itself and from the photometric features defining the available parameter space. Second, because the problem of defining a robust statistical method, always able to quantify and qualify the PDF estimation validity, is still an open issue. We present a comparison among PDFs obtained using three different methods on the same data set: two ML techniques, METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts) and ANNz2, plus the spectral energy distribution template fitting method, BPZ. The photometric data were extracted from the KiDS (Kilo Degree Survey) ESO Data Release 3, while the spectroscopy was obtained from the GAMA (Galaxy and Mass Assembly) Data Release 2. The statistical evaluation of both individual and stacked PDFs was done through quantitative and qualitative estimators, including a dummy PDF, useful to verify whether different statistical estimators can correctly assess PDF quality. We conclude that, in order to quantify the reliability and accuracy of any zphot PDF method, a combined set of statistical estimators is required.Comment: Accepted for publication by MNRAS, 20 pages, 14 figure

Residue depletion and histopathological alterations in gilthead sea bream (Sparus aurata) after oral administration of oxytetracycline

Aquaculture is a key component of the animal food industry, but intensive farming conditions increase the incidence of infectious diseases. Oxytetracycline (OTC) plays a major role for infectious diseases in fishes. Its MRLs include their 4-epimers, so in this trial, the depletion of residues of OTC and 4-epioxytetracycline in muscle and liver have been evaluated in gilthead sea bream (Sparus aurata) after oral administration. Hepatotoxicity has been investigated with histopathological effects on target tissues. A validated DAD-HPLC with SPE extraction has been applied. Residual levels in muscle and liver depleted with a similar kel, but mean retention time and t½ß resulted longer in muscle than in liver because of different vascularization. The OTC concentrations were below the LMR at 48 h after dosing. No analytical peaks ascribable to 4-epi-OTC or other derivatives were detected, while histopathology of liver showed degenerated parenchymal hepatocytes, nuclear pyknosis, focal necrosis and inflammatory leucocytes infiltration. It can be concluded that the assessment of pharmacokinetic and residual depletion of antibiotics result fundamental to determine the most suitable therapeutic regime and to minimize the toxic effects in fish species